Electronic learning (e-learning) and educational training in today's healthcare industry tend to focus on passive learning based on a linear presentation of medical information and a subsequently serialized testing of the presented medical information. For example, in a conventional method of presenting clinical training information over a computerized interface, a medical student, a paramedic trainee, a nursing trainee, or another healthcare industry professional is typically presented with linearly-broadcasted medical information that lacks interactive, hands-on, and empirical clinical training experience.
Unfortunately, the linearly-broadcasted medical information presented to a healthcare trainee is often detached from the reality of clinical practice, especially in case of diagnostic reasoning and differential diagnosis training for patient conditions and symptoms. In real-life clinical practice, a seasoned physician relies on his or her clinical knowledge and experience to conduct a time-efficient and educated guess for identifying a particular disease. In most circumstances, the seasoned physician does not linearly and exhaustively search through all possible differential diagnosis to identify the particular disease. The ability to identify a patient's disease correctly, based on one's own clinical knowledge and experience, is called “dynamic DDx,” or dynamic differential diagnosis. Conventional e-learning products in the healthcare industry are unable to instill trainees with dynamic differential diagnosis reasoning skills, which is an invaluable skill set in real-life clinical practice for an effective and rapid patient diagnosis.
Furthermore, conventional e-learning training and educational methods lack a convenient and scalable electronic platform for a healthcare educator to create, add, or change healthcare training contents that can nurture standard linear DDx reasoning skills, dynamic DDx reasoning skills, and/or other empirical clinical reasoning skills in healthcare industry trainees and medical students. At best, conventional e-learning solutions require the healthcare educator to modify or configure a substantial part of computer software codes or underlying software modules in order to change or update healthcare training contents.
In addition, conventional e-learning training and educational systems do not provide a computerized interactive suggestion interface to a healthcare educator to suggest and recommend additions or modifications to specific healthcare educational contents from a robust set of clinical research data sets that are dynamically linked to a healthcare content authoring platform.
Therefore, it may be desirable to provide a novel and intuitive healthcare content authoring software platform with an interactive suggestion interface, which a healthcare education content author can readily utilize to create and update computerized medical training and evaluation contents to medical students and trainees. Furthermore, it may also be desirable to provide a novel healthcare education content authoring assistant that enables nurturing of DDx-based reasoning skills and/or other empirical clinical reasoning skills in medical students and other healthcare industry professionals.